This article argues that search engines raise not merely technical issues but also political ones. Our study of search engines suggests that they systematically exclude (in some cases by design and in some, accidentally) certain sites and certain types of sites in favor of others, systematically giving prominence to some at the expense of others. We argue that such biases, which would lead to a narrowing of the Web's functioning in society, run counter to the basic architecture of the Web as well as to the values and ideals that have fueled widespread support for its growth and development. We consider ways of addressing the politics of search engines, raising doubts whether, in particular, the market mechanism could serve as an acceptable corrective.

/pdf/shaping-the-web-why-the-politics-of-search-engines-matters-3loerck3wl.pdf

Shaping the Web: Why the Politics of Search Engines Matters

Although the interest of a Web page is strictly related to its content and to the subjective readers' cultural background, a measure of the page authority can be provided that only depends on the topological structure of the Web. PageRank is a noticeable way to attach a score to Web pages on the basis of the Web connectivity. In this article, we look inside PageRank to disclose its fundamental properties concerning stability, complexity of computational scheme, and critical role of parameters involved in the computation. Moreover, we introduce a circuit analysis that allows us to understand the distribution of the page score, the way different Web communities interact each other, the role of dangling pages (pages with no outlinks), and the secrets for promotion of Web pages.

Inside PageRank

We present the results of our work that seek to negotiate the gap between low-level features and high-level concepts in the domain of web document retrieval. This work concerns a technique, called the latent semantic indexing (LSI), which has been used for textual information retrieval for many years. In this environment, LSI determines clusters of co-occurring keywords so that a query which uses a particular keyword can then retrieve documents perhaps not containing this keyword, but containing other keywords from the same cluster. In this paper, we examine the use of this technique for content-based web document retrieval, using both keywords and image features to represent the documents. Two different approaches to image feature representation, namely, color histograms and color anglograms, are adopted and evaluated. Experimental results show that LSI, together with both textual and visual features, is able to extract the underlying semantic structure of web documents, thus helping to improve the retrieval performance significantly, even when querying is done using only keywords.

https://www3.cs.stonybrook.edu/~rzhao/publications/TransMultimedia.pdf

Narrowing the semantic gap - improved text-based web document retrieval using visual features

An Introduction to Search Engines and Web Navigation: Levene/Search Engines

Purpose – The purpose of this paper is to identify the most popular techniques used to rank a web page highly in Google.Design/methodology/approach – The paper presents the results of a study into 50 highly optimized web pages that were created as part of a Search Engine Optimization competition. The study focuses on the most popular techniques that were used to rank highest in this competition, and includes an analysis on the use of PageRank, number of pages, number of in‐links, domain age and the use of third party sites such as directories and social bookmarking sites. A separate study was made into 50 non‐optimized web pages for comparison.Findings – The paper provides insight into the techniques that successful Search Engine Optimizers use to ensure a page ranks highly in Google. Recognizes the importance of PageRank and links as well as directories and social bookmarking sites.Research limitations/implications – Only the top 50 web sites for a specific query were analyzed. Analysing more web sites a...

Analysing Google rankings through search engine optimization data

What is a Tall Poppy Among Web Pages

Search engines and indices were created to help people find information amongst the rapidly increasing number of World Wide Web (WWW) pages. The search engines automatically visit and index pages so that they can retum good matches for their users' queries. The way that this indexing is done varies from engine to engine and the detail is usually secret although the strategy is sometimes made public in general terms. The search engines' aim is to return relevant pages quickly. On the other hand, the author of a Web page has a vested interest in it rating highly, for appropriate queries, on as many search engines as possible. Some authors have an interest in their page rating well for a great many types of query indeed—spamming has come to the Web. We treat modelling the workings of WWW search engines as an inductive inference problem. A training set of data is collected, being pages returned in response to typical queries. Decision trees are used as the model class for the search engines' selection criteria although this is not to say that search engines actually contain decision trees. A machine learning program is used to infer a decision tree for each search engine, an information-theory criterion being used to direct the inference and to prevent over-fitting.

Glen Pringle

Papers

What is a Tall Poppy Among Web Pages

What is a tall poppy among Web pages